Summary
Daniel Scott is a machine learning engineer and computational neuroscientist with 11 years of experience bridging reinforcement learning, neural networks, and statistical models for applications in neuroscience, psychiatry, genetics, and AI. He completed a PhD and postdoc at Brown University where he led research on neural reinforcement learning, plasticity, and computational psychiatry, published original work, reviewed for top journals, and organized international events. In industry he has designed biomedical meta-science systems, built custom graph and vector DB integrations, evaluated memory retrieval strategies, and currently focuses on agent benchmarking and evaluation. Comfortable across research and production, he pairs deep mathematical training (physics and math degrees from UC Berkeley) with hands-on engineering to move models from theory to deployed systems. He’s notable for an unusually broad graduate coursework load in applied math and physics and a track record of translating cognitive models into practical ML tooling. Based in Providence, RI, he brings a rare mix of domain expertise in brain-inspired learning and pragmatic system-building for biomedical and AI applications.
11 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at University of California, Berkeley
Postdoc, Neuroscience, Postdoc, Neuroscience at Brown University
Diablo Valley College